Structured-Light Depth Estimation Using Probability Matrix Matching
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Solution Overview
Problem
Structured-light 3D-camera systems face challenges in real-time depth estimation due to high computational costs and noise sensitivity, especially when image patch size and search range are large, leading to inefficiencies and inaccuracies.
Innovation Solution
The method involves using a lookup table with a probability matrix to match image patches with reference light patterns by binarizing pixels, forming vectors, and determining class correspondence, which reduces computational burden and noise, enabling fast and accurate depth estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the image patch size and searching range are increased to improve depth estimation accuracy, then the measurement precision is improved, but the computational time and processing complexity increase significantly
Solution Approach 1:
The patent divides the image into multiple patches and processes each patch independently through binarization and classification. By segmenting the overall depth estimation task into smaller patch-level operations, the system can process multiple regions in parallel, reducing total computational time while maintaining accurate depth estimation for each patch through the probability matrix matching approach
Solution Approach 2:
The patent performs binarization of image patches before the matching process, converting grayscale patch data into binary representations. This preliminary action simplifies the subsequent probability matrix multiplication and classification steps, reducing the computational complexity of the main depth estimation algorithm while preserving the essential pattern information needed for accurate depth calculation
2Measurement precision
If the image patch size and searching range are increased to improve depth estimation accuracy, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent transforms the continuous grayscale pixel values in image patches into binary parameters (0 or 1) through binarization. This parameter change simplifies the data structure and reduces the complexity of subsequent processing operations. The binary representation allows for efficient probability matrix multiplication and classification, maintaining depth estimation accuracy while significantly reducing processing complexity
Solution Approach 2:
The patent replaces complex iterative matching algorithms with a direct probability matrix multiplication approach. Instead of using traditional mechanical search methods to find the best match between patches and reference patterns, the system uses probabilistic classification through matrix operations, which are computationally more efficient and reduce processing complexity while maintaining measurement precision
3Measurement precision
If traditional patch matching methods are used to ensure accurate depth estimation, then the measurement precision is maintained, but the power consumption increases
Solution Approach 1:
The patent segments the image processing into independent patch operations that can be executed efficiently. By processing small binary patches through simple probability matrix multiplication rather than performing exhaustive traditional matching, the system reduces the total number of computational operations required, thereby lowering power consumption while maintaining depth estimation accuracy through the probabilistic classification approach
Solution Approach 2:
The patent uses simple binary representations of image patches instead of storing and processing complex grayscale or color information. This disposable simplification of data representation reduces memory bandwidth requirements and processing energy. The binary patches are processed quickly through probability matrix operations, achieving accurate depth estimation with minimal energy expenditure compared to traditional methods
4Measurement precision
If traditional structured-light systems are used to perform depth estimation, then the measurement capability is provided, but the system becomes sensitive to image flaws such as pixel noise, blur, distortion and saturation
Solution Approach 1:
The patent applies binarization to transform grayscale patch data into binary form, which fundamentally changes the parameter space. This transformation makes the system robust to various image flaws because binary representation is inherently more resistant to noise and distortion. The probability matrix matching approach further enhances robustness by considering probabilistic relationships rather than exact pixel matches, allowing accurate depth estimation even when image quality is degraded
Solution Approach 2:
The patent converts the potential harm of image noise and distortion into a benefit by using probability-based matching. Instead of requiring perfect pixel correspondence, the system uses probability matrices that can accommodate variations and errors in the captured image. This probabilistic approach transforms the sensitivity to image flaws into a robustness feature, where the system can still achieve accurate depth estimation even with degraded image quality
Data Source
AI summary
A system and a method are disclosed for a structured-light system to estimate depth in an image. An image is received in which the image is of a scene onto which a reference light pattern has been projected. The projection of the reference light pattern includes a predetermined number of particular sub-patterns. A patch of the received image and a sub-pattern of the reference light pattern are matched based on either a hardcode template matching technique or a probability that the patch corresponds to the sub-pattern. If a lookup table is used, the table may be a probability matrix, may contain precomputed correlations scores or may contain precomputed class IDs. An estimate of depth of the patch is determined based on a disparity between the patch and the sub-pattern.